Defining targets for training images
Cleaning up commit message, and formatting on files.
More details:
- Includes ability to run command-line with or without bazel
- Also has write capability to flatbuffers for multiple targets
- Definition of targets and cameras, along with 3D point calcs
- Includes camera and target helper functions, along with defaults to get started
- Modified from Brian's version to work with python target_definition code, esp. to send multiple targets.
- Getting target_defintion set up for building flatbuffer data file. Updated blue power point image to include full wings.
- Sample python code to do multiple image training, matching, and display results. Includes sample images to play with
- Adding code to define masking polygons for removing features for training
- Tools to write out the target definition to flatbuffer
- Adds multi-target definition, and automates the process
Change-Id: Ibc90d51a129751bf456da6813b3e7cbc5e55901a
diff --git a/y2020/vision/tools/python_code/BUILD b/y2020/vision/tools/python_code/BUILD
new file mode 100644
index 0000000..670b664
--- /dev/null
+++ b/y2020/vision/tools/python_code/BUILD
@@ -0,0 +1,49 @@
+load("@com_github_google_flatbuffers//:build_defs.bzl", "flatbuffer_cc_library", "flatbuffer_py_library")
+
+py_binary(
+ name = "load_sift_training",
+ data = [
+ ":test_images/train_power_port_red.png",
+ ":test_images/train_power_port_red_webcam.png",
+ ":test_images/train_power_port_blue.png",
+ ":test_images/train_loading_bay_red.png",
+ ":test_images/train_loading_bay_blue.png",
+ ],
+ srcs = ["load_sift_training.py",
+ "camera_definition.py",
+ "define_training_data.py",
+ "target_definition.py",
+ "train_and_match.py",
+ ],
+ args = ["sift_training_data.h",
+ ],
+ default_python_version = "PY3",
+ srcs_version = "PY2AND3",
+ deps = [
+ "//y2020/vision/sift:sift_fbs_python",
+ "@opencv_contrib_nonfree_amd64//:python_opencv",
+ "@bazel_tools//tools/python/runfiles",
+ ],
+)
+
+genrule(
+ name = "run_load_sift_training",
+ outs = [
+ "sift_training_data.h",
+ ],
+ cmd = " ".join([
+ "$(location :load_sift_training)",
+ "$(location sift_training_data.h)",
+ ]),
+ tools = [
+ ":load_sift_training",
+ ],
+)
+
+cc_library(
+ name = "sift_training",
+ hdrs = [
+ "sift_training_data.h",
+ ],
+ visibility = ["//visibility:public"],
+)
diff --git a/y2020/vision/tools/python_code/define_training_data.py b/y2020/vision/tools/python_code/define_training_data.py
index d1802aa..22eb6ce 100644
--- a/y2020/vision/tools/python_code/define_training_data.py
+++ b/y2020/vision/tools/python_code/define_training_data.py
@@ -5,29 +5,33 @@
import numpy as np
import time
-import field_display
import train_and_match as tam
# Points for current polygon
point_list = []
-current_mouse = (0,0)
+current_mouse = (0, 0)
+
def get_mouse_event(event, x, y, flags, param):
global point_list
global current_mouse
- current_mouse = (x,y)
+ current_mouse = (x, y)
if event == cv2.EVENT_LBUTTONUP:
#print("Adding point at %d, %d" % (x,y))
- point_list.append([x,y])
+ point_list.append([x, y])
+ pass
-def draw_polygon(image, polygon, color=(255,0,0), close_polygon = False):
+
+def draw_polygon(image, polygon, color=(255, 0, 0), close_polygon=False):
for point in polygon:
- image = cv2.circle(image, (point[0], point[1]), 5, (255,0,0), -1)
- if len(polygon) > 1:
+ image = cv2.circle(image, (point[0], point[1]), 5, (255, 0, 0), -1)
+ if (len(polygon) > 1):
np_poly = np.array(polygon)
- image = cv2.polylines(image, [np_poly], close_polygon, color, thickness=3)
+ image = cv2.polylines(
+ image, [np_poly], close_polygon, color, thickness=3)
return image
+
# Close out polygon, return True if size is 3 or more points
def finish_polygon(image, polygon):
global point_list
@@ -36,7 +40,7 @@
return False
point_list.append(point_list[0])
- image = draw_polygon(image, point_list, color=(0,0,255))
+ image = draw_polygon(image, point_list, color=(0, 0, 255))
cv2.imshow("image", image)
cv2.waitKey(500)
return True
@@ -99,14 +103,23 @@
cv2.namedWindow("image")
cv2.setMouseCallback("image", get_mouse_event)
- while len(point_list) < len(points):
+ while (len(point_list) < len(points)):
i = len(point_list)
# Draw mouse location and suggested target
display_image = image.copy()
- display_image = cv2.circle(display_image, (points[i][0], points[i][1]), 15, (0,255,0), 2)
+ display_image = cv2.circle(display_image, (points[i][0], points[i][1]),
+ 15, (0, 255, 0), 2)
cursor_length = 5
- display_image = cv2.line(display_image, (current_mouse[0]-cursor_length, current_mouse[1]), (current_mouse[0]+cursor_length, current_mouse[1]), (255,0,0), 2, cv2.LINE_AA)
- display_image = cv2.line(display_image, (current_mouse[0], current_mouse[1]-cursor_length), (current_mouse[0], current_mouse[1]+cursor_length), (255,0,0), 2, cv2.LINE_AA)
+ display_image = cv2.line(
+ display_image,
+ (current_mouse[0] - cursor_length, current_mouse[1]),
+ (current_mouse[0] + cursor_length, current_mouse[1]), (255, 0, 0),
+ 2, cv2.LINE_AA)
+ display_image = cv2.line(
+ display_image,
+ (current_mouse[0], current_mouse[1] - cursor_length),
+ (current_mouse[0], current_mouse[1] + cursor_length), (255, 0, 0),
+ 2, cv2.LINE_AA)
cv2.imshow("image", display_image)
@@ -179,7 +192,7 @@
pts_2d_lstsq = append_ones(pts_2d)
pts_3d_lstsq = np.asarray(np.float32(polygon_3d)).reshape(-1,3)
- reprojection_map = np.linalg.lstsq(pts_2d_lstsq, pts_3d_lstsq, rcond=None)[0]
+ reprojection_map = np.linalg.lstsq(pts_2d_lstsq, pts_3d_lstsq, rcond=-1)[0]
return reprojection_map
@@ -198,19 +211,24 @@
# Compute camera location
# TODO: Warn on bad inliers
# TODO: Change this to not have to recast to np
- pts_2d_np = np.asarray(np.float32(pts_2d)).reshape(-1,1,2)
- pts_3d_np = np.asarray(np.float32(pts_3d)).reshape(-1,1,3)
- retval, R, T, inliers = cv2.solvePnPRansac(pts_3d_np, pts_2d_np, cam_mat, distortion_coeffs)
- pts_3d_proj_2d, jac_2d = cv2.projectPoints(pts_3d_np, R, T, cam_mat, distortion_coeffs)
+ pts_2d_np = np.asarray(np.float32(pts_2d)).reshape(-1, 1, 2)
+ pts_3d_np = np.asarray(np.float32(pts_3d)).reshape(-1, 1, 3)
+ retval, R, T, inliers = cv2.solvePnPRansac(pts_3d_np, pts_2d_np, cam_mat,
+ distortion_coeffs)
+ pts_3d_proj_2d, jac_2d = cv2.projectPoints(pts_3d_np, R, T, cam_mat,
+ distortion_coeffs)
+ if inliers is None:
+ print("WARNING: Didn't get any inliers when reprojecting polygons")
+ return img
for i in range(len(pts_2d)):
pt_2d = pts_2d_np[i][0]
pt_3d_proj = pts_3d_proj_2d[i][0]
- pt_color = (0,255,0)
+ pt_color = (0, 255, 0)
if i not in inliers:
- pt_color = (0,0,255)
+ pt_color = (0, 0, 255)
- img = cv2.circle(img,(pt_2d[0],pt_2d[1]),3,pt_color,3)
- img = cv2.circle(img,(pt_3d_proj[0], pt_3d_proj[1]),15,pt_color,3)
+ img = cv2.circle(img, (pt_2d[0], pt_2d[1]), 3, pt_color, 3)
+ img = cv2.circle(img, (pt_3d_proj[0], pt_3d_proj[1]), 15, pt_color, 3)
cv2.imshow("image", img)
cv2.waitKey(0)
diff --git a/y2020/vision/tools/python_code/load_sift_training.py b/y2020/vision/tools/python_code/load_sift_training.py
new file mode 100644
index 0000000..9fa4acf
--- /dev/null
+++ b/y2020/vision/tools/python_code/load_sift_training.py
@@ -0,0 +1,100 @@
+#!/usr/bin/python3
+
+import cv2
+import sys
+import flatbuffers
+import target_definition
+
+import frc971.vision.sift.TrainingImage as TrainingImage
+import frc971.vision.sift.TrainingData as TrainingData
+import frc971.vision.sift.Feature as Feature
+
+def main():
+
+ output_path = sys.argv[1]
+ print("Writing file to ", output_path)
+
+ target_data_list = target_definition.compute_target_definition()
+
+ fbb = flatbuffers.Builder(0)
+
+ images_vector = []
+
+ for target_data in target_data_list:
+
+ features_vector = []
+
+ for keypoint, keypoint_3d, descriptor in zip(target_data.keypoint_list,
+ target_data.keypoint_list_3d,
+ target_data.descriptor_list):
+
+ Feature.FeatureStartDescriptorVector(fbb, len(descriptor))
+ for n in reversed(descriptor):
+ fbb.PrependFloat32(n)
+ descriptor_vector = fbb.EndVector(len(descriptor))
+
+ Feature.FeatureStart(fbb)
+
+ Feature.FeatureAddDescriptor(fbb, descriptor_vector)
+ Feature.FeatureAddX(fbb, keypoint.pt[0])
+ Feature.FeatureAddY(fbb, keypoint.pt[1])
+ Feature.FeatureAddSize(fbb, keypoint.size)
+ Feature.FeatureAddAngle(fbb, keypoint.angle)
+ Feature.FeatureAddResponse(fbb, keypoint.response)
+ Feature.FeatureAddOctave(fbb, keypoint.octave)
+
+ features_vector.append(Feature.FeatureEnd(fbb))
+
+ ## TODO: Write 3d vector here
+
+ TrainingImage.TrainingImageStartFeaturesVector(fbb, len(features_vector))
+ for feature in reversed(features_vector):
+ fbb.PrependUOffsetTRelative(feature)
+ features_vector_table = fbb.EndVector(len(features_vector))
+
+ TrainingImage.TrainingImageStart(fbb)
+ TrainingImage.TrainingImageAddFeatures(fbb, features_vector_table)
+ # TODO(Brian): Fill out the transformation matrices.
+ images_vector.append(TrainingImage.TrainingImageEnd(fbb))
+
+ TrainingData.TrainingDataStartImagesVector(fbb, len(images_vector))
+ for training_image in reversed(images_vector):
+ fbb.PrependUOffsetTRelative(training_image)
+ images_vector_table = fbb.EndVector(len(images_vector))
+
+ TrainingData.TrainingDataStart(fbb)
+ TrainingData.TrainingDataAddImages(fbb, images_vector_table)
+ fbb.Finish(TrainingData.TrainingDataEnd(fbb))
+
+ bfbs = fbb.Output()
+
+ output_prefix = [
+ b'#ifndef Y2020_VISION_TOOLS_PYTHON_CODE_TRAINING_DATA_H_',
+ b'#define Y2020_VISION_TOOLS_PYTHON_CODE_TRAINING_DATA_H_',
+ b'#include <string_view>',
+ b'namespace frc971 {',
+ b'namespace vision {',
+ b'inline std::string_view SiftTrainingData() {',
+ ]
+ output_suffix = [
+ b' return std::string_view(kData, sizeof(kData));',
+ b'}',
+ b'} // namespace vision',
+ b'} // namespace frc971',
+ b'#endif // Y2020_VISION_TOOLS_PYTHON_CODE_TRAINING_DATA_H_',
+ ]
+
+ with open(output_path, 'wb') as output:
+ for line in output_prefix:
+ output.write(line)
+ output.write(b'\n')
+ output.write(b'alignas(64) static constexpr char kData[] = "')
+ for byte in fbb.Output():
+ output.write(b'\\x' + (b'%x' % byte).zfill(2))
+ output.write(b'";\n')
+ for line in output_suffix:
+ output.write(line)
+ output.write(b'\n')
+
+if __name__ == '__main__':
+ main()
diff --git a/y2020/vision/tools/python_code/target_definition.py b/y2020/vision/tools/python_code/target_definition.py
index d711fb9..779eb0b 100644
--- a/y2020/vision/tools/python_code/target_definition.py
+++ b/y2020/vision/tools/python_code/target_definition.py
@@ -1,3 +1,4 @@
+import argparse
import cv2
import json
import math
@@ -7,15 +8,27 @@
import define_training_data as dtd
import train_and_match as tam
-VISUALIZE_KEYPOINTS = True
-VISUALIZE_POLYGONS = True
+global VISUALIZE_KEYPOINTS
+global USE_BAZEL
+USE_BAZEL = True
+VISUALIZE_KEYPOINTS = False
+def bazel_name_fix(filename):
+ ret_name = filename
+ if USE_BAZEL:
+ ret_name = 'org_frc971/y2020/vision/tools/python_code/' + filename
+
+ return ret_name
class TargetData:
def __init__(self, filename):
self.image_filename = filename
# Load an image (will come in as a 1-element list)
- self.image = tam.load_images([filename])[0]
+ if USE_BAZEL:
+ from bazel_tools.tools.python.runfiles import runfiles
+ r = runfiles.Create()
+ self.image_filename = r.Rlocation(bazel_name_fix(self.image_filename))
+ self.image = tam.load_images([self.image_filename])[0]
self.polygon_list = []
self.polygon_list_3d = []
self.reprojection_map_list = []
@@ -60,226 +73,342 @@
return point_list_3d
- # Save out the training data-- haven't implemented this
- def save_training_data(self):
- if (len(self.keypoint_list) != len(self.descriptor_list)
- or len(self.keypoint_list) != len(self.keypoint_list_3d)):
- print("Big problem-- lists don't match in size: %d, %d, %d" %
- (len(self.keypoint_list), len(self.descriptor_list),
- len(self.keypoint_list_3d)))
+def compute_target_definition():
+ ############################################################
+ # TARGET DEFINITIONS
+ ############################################################
- print("(Would like to) Save target with %d keypoints" % len(
- self.keypoint_list))
+ ideal_target_list = []
+ training_target_list = []
+ # Some general info about our field and targets
+ # Assume camera centered on target at 1 m above ground and distance of 4.85m
+ field_length = 15.98
+ power_port_total_height = 3.10
+ power_port_center_y = 1.67
+ power_port_width = 1.22
+ power_port_bottom_wing_height = 1.88
+ power_port_wing_width = 1.83
+ loading_bay_edge_y = 1.11
+ loading_bay_width = 1.52
+ loading_bay_height = 0.94
-# TARGET DEFINITIONS
+ # Pick the target center location at halfway between top and bottom of the top panel
+ target_center_height = (power_port_total_height + power_port_bottom_wing_height) / 2.
-# Some general info about our field and targets
-# Assume camera centered on target at 1 m above ground and distance of 4.85m
-camera_height = 1.
-total_target_height = 3.10
-field_length = 15.98
-power_port_offset_y = 1.67
-power_port_width = 1.22
-bottom_wing_height = 1.88
-wing_width = 1.83
+ # TODO: Still need to figure out what this angle actually is
+ wing_angle = 20. * math.pi / 180.
-# Pick the target center location at halfway between top and bottom of the top panel
-target_center_height = (total_target_height + bottom_wing_height) / 2.
+ ###
+ ### Red Power Port
+ ###
-# TODO: Still need to figure out what this angle actually is
-wing_angle = 20. * math.pi / 180.
+ # Create the reference "ideal" image
+ ideal_power_port_red = TargetData('test_images/train_power_port_red.png')
-# Start at lower left corner, and work around clockwise
-# These are taken by manually finding the points in gimp for this image
-main_panel_polygon_points_2d = [(451, 679), (451, 304), (100, 302), (451, 74),
- (689, 74), (689, 302), (689, 679)]
-# These are "virtual" 3D points based on the expected geometry
-main_panel_polygon_points_3d = [
- (field_length, power_port_width / 2. - power_port_offset_y,
- 0.), (field_length, power_port_width / 2. - power_port_offset_y,
- bottom_wing_height),
- (field_length, power_port_width / 2. - power_port_offset_y + wing_width,
- bottom_wing_height),
- (field_length, power_port_width / 2. - power_port_offset_y,
- total_target_height), (field_length,
- -power_port_width / 2. - power_port_offset_y,
- total_target_height),
- (field_length, -power_port_width / 2. - power_port_offset_y,
- bottom_wing_height), (field_length,
- -power_port_width / 2. - power_port_offset_y, 0.)
-]
+ # Start at lower left corner, and work around clockwise
+ # These are taken by manually finding the points in gimp for this image
+ power_port_red_main_panel_polygon_points_2d = [(451, 679), (451, 304),
+ (100, 302), (451, 74),
+ (689, 74), (689, 302),
+ (689, 679)]
-wing_panel_polygon_points_2d = [(689, 74), (1022, 302), (689, 302)]
-# These are "virtual" 3D points based on the expected geometry
-wing_panel_polygon_points_3d = [
- (field_length, -power_port_width / 2. - power_port_offset_y,
- total_target_height),
- (field_length - wing_width * math.sin(wing_angle), -power_port_width / 2. -
- power_port_offset_y - wing_width * math.cos(wing_angle),
- bottom_wing_height), (field_length,
- -power_port_width / 2. - power_port_offset_y,
- bottom_wing_height)
-]
+ # These are "virtual" 3D points based on the expected geometry
+ power_port_red_main_panel_polygon_points_3d = [
+ (field_length, -power_port_center_y + power_port_width / 2., 0.),
+ (field_length, -power_port_center_y + power_port_width / 2.,
+ power_port_bottom_wing_height),
+ (field_length, -power_port_center_y + power_port_width / 2.
+ + power_port_wing_width, power_port_bottom_wing_height),
+ (field_length, -power_port_center_y + power_port_width / 2.,
+ power_port_total_height),
+ (field_length, -power_port_center_y - power_port_width / 2.,
+ power_port_total_height),
+ (field_length, -power_port_center_y - power_port_width / 2.,
+ power_port_bottom_wing_height),
+ (field_length, -power_port_center_y - power_port_width / 2., 0.)
+ ]
-# Populate the red power port
-ideal_power_port_red = TargetData('test_images/train_power_port_red.png')
+ power_port_red_wing_panel_polygon_points_2d = [(689, 74), (1022, 302),
+ (689, 302)]
+ # These are "virtual" 3D points based on the expected geometry
+ power_port_red_wing_panel_polygon_points_3d = [
+ (field_length, -power_port_center_y - power_port_width / 2.,
+ power_port_total_height),
+ (field_length - power_port_wing_width * math.sin(wing_angle),
+ -power_port_center_y - power_port_width / 2.
+ - power_port_wing_width * math.cos(wing_angle),
+ power_port_bottom_wing_height),
+ (field_length, -power_port_center_y - power_port_width / 2.,
+ power_port_bottom_wing_height)
+ ]
-ideal_power_port_red.polygon_list.append(main_panel_polygon_points_2d)
-ideal_power_port_red.polygon_list_3d.append(main_panel_polygon_points_3d)
+ # Populate the red power port
+ ideal_power_port_red.polygon_list.append(power_port_red_main_panel_polygon_points_2d)
+ ideal_power_port_red.polygon_list_3d.append(power_port_red_main_panel_polygon_points_3d)
-ideal_power_port_red.polygon_list.append(wing_panel_polygon_points_2d)
-ideal_power_port_red.polygon_list_3d.append(wing_panel_polygon_points_3d)
+ ideal_power_port_red.polygon_list.append(power_port_red_wing_panel_polygon_points_2d)
+ ideal_power_port_red.polygon_list_3d.append(power_port_red_wing_panel_polygon_points_3d)
-ideal_target_list = []
-ideal_target_list.append(ideal_power_port_red)
+ # Add the ideal 3D target to our list
+ ideal_target_list.append(ideal_power_port_red)
+ # And add the training image we'll actually use to the training list
+ training_target_list.append(TargetData('test_images/train_power_port_red_webcam.png'))
-# Create feature extractor
-feature_extractor = tam.load_feature_extractor()
+ ###
+ ### Red Loading Bay
+ ###
-# Use webcam parameters for now
-camera_params = camera_definition.web_cam_params
-for ideal_target in ideal_target_list:
- print("Preparing target for image %s" % ideal_target.image_filename)
- ideal_target.extract_features(feature_extractor)
- ideal_target.filter_keypoints_by_polygons()
- ideal_target.compute_reprojection_maps()
- ideal_target.keypoint_list_3d = ideal_target.project_keypoint_to_3d_by_polygon(
- ideal_target.keypoint_list)
+ ideal_loading_bay_red = TargetData('test_images/train_loading_bay_red.png')
- if VISUALIZE_POLYGONS:
- # For each polygon, show the 2D points and the reprojection for 3D
- # of the polygon definition
- for polygon_2d, polygon_3d in zip(ideal_target.polygon_list,
- ideal_target.polygon_list_3d):
- ideal_pts_tmp = np.asarray(polygon_2d).reshape(-1, 2)
- ideal_pts_3d_tmp = np.asarray(polygon_3d).reshape(-1, 3)
- # We can only compute pose if we have at least 4 points
- # Only matters for reprojection for visualization
- # Keeping this code here, since it's helpful when testing
- if (len(polygon_2d) >= 4):
- img_copy = dtd.draw_polygon(ideal_target.image.copy(), polygon_2d, (0,255,0), True)
- dtd.visualize_reprojections(img_copy, ideal_pts_tmp, ideal_pts_3d_tmp, camera_params.camera_int.camera_matrix, camera_params.camera_int.distortion_coeffs)
+ # Start at lower left corner, and work around clockwise
+ # These are taken by manually finding the points in gimp for this image
+ loading_bay_red_polygon_points_2d = [(42, 406), (42, 35), (651, 34), (651, 406)]
- if VISUALIZE_KEYPOINTS:
- # For each polygon, show which keypoints (2D and 3D manual) were kept
- for polygon in ideal_target.polygon_list:
- img_copy = ideal_target.image.copy()
- kp_in_poly2d = []
- kp_in_poly3d = []
- for kp, kp_3d in zip(ideal_target.keypoint_list,
- ideal_target.keypoint_list_3d):
- if dtd.point_in_polygons((kp.pt[0], kp.pt[1]), [polygon]):
- kp_in_poly2d.append((kp.pt[0], kp.pt[1]))
- kp_in_poly3d.append(kp_3d)
+ # These are "virtual" 3D points based on the expected geometry
+ loading_bay_red_polygon_points_3d = [
+ (field_length, loading_bay_edge_y + loading_bay_width, 0.),
+ (field_length, loading_bay_edge_y + loading_bay_width, loading_bay_height),
+ (field_length, loading_bay_edge_y, loading_bay_height),
+ (field_length, loading_bay_edge_y, 0.)
+ ]
- img_copy = dtd.draw_polygon(img_copy, polygon, (0,255,0), True)
- dtd.visualize_reprojections(
- img_copy,
- np.asarray(kp_in_poly2d).reshape(-1, 2),
- np.asarray(kp_in_poly3d).reshape(
- -1, 3), camera_params.camera_int.camera_matrix,
- camera_params.camera_int.distortion_coeffs)
+ ideal_loading_bay_red.polygon_list.append(loading_bay_red_polygon_points_2d)
+ ideal_loading_bay_red.polygon_list_3d.append(loading_bay_red_polygon_points_3d)
-### TODO: Add code to do manual point selection
-AUTO_PROJECTION = True
-training_target_list = []
-if AUTO_PROJECTION:
- print("Auto projection of training keypoints to 3D using ideal images")
- # Match the captured training image against the "ideal" training image
- # and use those matches to pin down the 3D locations of the keypoints
+ ideal_target_list.append(ideal_loading_bay_red)
+ training_target_list.append(TargetData('test_images/train_loading_bay_red.png'))
- training_power_port_red = TargetData(
- 'test_images/train_power_port_red_webcam.png')
+ ###
+ ### Blue Power Port
+ ###
- training_target_list.append(training_power_port_red)
+ ideal_power_port_blue = TargetData('test_images/train_power_port_blue.png')
- for target_ind in range(len(training_target_list)):
- # Assumes we have 1 ideal view for each training target
- training_target = training_target_list[target_ind]
- ideal_target = ideal_target_list[target_ind]
+ # Start at lower left corner, and work around clockwise
+ # These are taken by manually finding the points in gimp for this image
+ power_port_blue_main_panel_polygon_points_2d = [(438, 693), (438, 285),
+ (93, 285), (440, 50),
+ (692, 50), (692, 285),
+ (692, 693)]
- print("Preparing target for image %s" % training_target.image_filename)
- # Extract keypoints and descriptors for model
- training_target.extract_features(feature_extractor)
+ # These are "virtual" 3D points based on the expected geometry
+ power_port_blue_main_panel_polygon_points_3d = [
+ (0., power_port_center_y - power_port_width / 2., 0.),
+ (0., power_port_center_y - power_port_width / 2.,
+ power_port_bottom_wing_height),
+ (0., power_port_center_y - power_port_width / 2. - power_port_wing_width,
+ power_port_bottom_wing_height),
+ (0., power_port_center_y - power_port_width / 2.,
+ power_port_total_height),
+ (0., power_port_center_y + power_port_width / 2.,
+ power_port_total_height),
+ (0., power_port_center_y + power_port_width / 2.,
+ power_port_bottom_wing_height),
+ (0., power_port_center_y + power_port_width / 2., 0.)
+ ]
- # Create matcher that we'll use to match with ideal
- matcher = tam.train_matcher([training_target.descriptor_list])
+ power_port_blue_wing_panel_polygon_points_2d = [(692, 50), (1047, 285),
+ (692, 285)]
+ # These are "virtual" 3D points based on the expected geometry
+ power_port_blue_wing_panel_polygon_points_3d = [
+ (0., power_port_center_y + power_port_width / 2.,
+ power_port_total_height),
+ (power_port_wing_width * math.sin(wing_angle),
+ power_port_center_y - power_port_width / 2. +
+ power_port_wing_width * math.cos(wing_angle),
+ power_port_bottom_wing_height),
+ (0., power_port_center_y + power_port_width / 2.,
+ power_port_bottom_wing_height)
+ ]
- matches_list = tam.compute_matches(matcher,
- [training_target.descriptor_list],
- [ideal_target.descriptor_list])
+ # Populate the blue power port
+ ideal_power_port_blue.polygon_list.append(power_port_blue_main_panel_polygon_points_2d)
+ ideal_power_port_blue.polygon_list_3d.append(power_port_blue_main_panel_polygon_points_3d)
- homography_list, matches_mask_list = tam.compute_homographies(
- [training_target.keypoint_list], [ideal_target.keypoint_list],
- matches_list)
+ ideal_power_port_blue.polygon_list.append(power_port_blue_wing_panel_polygon_points_2d)
+ ideal_power_port_blue.polygon_list_3d.append(power_port_blue_wing_panel_polygon_points_3d)
- for polygon in ideal_target.polygon_list:
- ideal_pts_2d = np.asarray(np.float32(polygon)).reshape(-1, 1, 2)
- H_inv = np.linalg.inv(homography_list[0])
- # We use the ideal target's polygons to define the polygons on
- # the training target
- transformed_polygon = cv2.perspectiveTransform(ideal_pts_2d, H_inv)
- training_target.polygon_list.append(transformed_polygon)
+ ideal_target_list.append(ideal_power_port_blue)
+ training_target_list.append(TargetData('test_images/train_power_port_blue.png'))
- # Verify that this looks right
+ ###
+ ### Blue Loading Bay
+ ###
+
+ ideal_loading_bay_blue = TargetData('test_images/train_loading_bay_blue.png')
+
+ # Start at lower left corner, and work around clockwise
+ # These are taken by manually finding the points in gimp for this image
+ loading_bay_blue_polygon_points_2d = [(7, 434), (7, 1), (729, 1), (729, 434)]
+
+ # These are "virtual" 3D points based on the expected geometry
+ loading_bay_blue_polygon_points_3d = [
+ (field_length, loading_bay_edge_y + loading_bay_width, 0.),
+ (field_length, loading_bay_edge_y + loading_bay_width, loading_bay_height),
+ (field_length, loading_bay_edge_y, loading_bay_height),
+ (field_length, loading_bay_edge_y, 0.)
+ ]
+
+ ideal_loading_bay_blue.polygon_list.append(loading_bay_blue_polygon_points_2d)
+ ideal_loading_bay_blue.polygon_list_3d.append(loading_bay_blue_polygon_points_3d)
+
+ ideal_target_list.append(ideal_loading_bay_blue)
+ training_target_list.append(TargetData('test_images/train_loading_bay_blue.png'))
+
+ # Create feature extractor
+ feature_extractor = tam.load_feature_extractor()
+
+ # Use webcam parameters for now
+ camera_params = camera_definition.web_cam_params
+
+ for ideal_target in ideal_target_list:
+ print("\nPreparing target for image %s" % ideal_target.image_filename)
+ ideal_target.extract_features(feature_extractor)
+ ideal_target.filter_keypoints_by_polygons()
+ ideal_target.compute_reprojection_maps()
+ ideal_target.keypoint_list_3d = ideal_target.project_keypoint_to_3d_by_polygon(
+ ideal_target.keypoint_list)
+
+ if VISUALIZE_KEYPOINTS:
+ for i in range(len(ideal_target.polygon_list)):
+ ideal_pts_tmp = np.asarray(ideal_target.polygon_list[i]).reshape(
+ -1, 2)
+ ideal_pts_3d_tmp = np.asarray(
+ ideal_target.polygon_list_3d[i]).reshape(-1, 3)
+ # We can only compute pose if we have at least 4 points
+ # Only matters for reprojection for visualization
+ # Keeping this code here, since it's helpful when testing
+ if (len(ideal_target.polygon_list[i]) >= 4):
+ img_copy = dtd.draw_polygon(ideal_target.image.copy(), ideal_target.polygon_list[i], (0,255,0), True)
+ dtd.visualize_reprojections(img_copy, ideal_pts_tmp, ideal_pts_3d_tmp, camera_params.camera_int.camera_matrix, camera_params.camera_int.distortion_coeffs)
+
+ for polygon in ideal_target.polygon_list:
+ img_copy = ideal_target.image.copy()
+ kp_in_poly2d = []
+ kp_in_poly3d = []
+ for kp, kp_3d in zip(ideal_target.keypoint_list,
+ ideal_target.keypoint_list_3d):
+ if dtd.point_in_polygons((kp.pt[0], kp.pt[1]), [polygon]):
+ kp_in_poly2d.append((kp.pt[0], kp.pt[1]))
+ kp_in_poly3d.append(kp_3d)
+
+ dtd.visualize_reprojections(
+ img_copy,
+ np.asarray(kp_in_poly2d).reshape(-1, 2),
+ np.asarray(kp_in_poly3d).reshape(
+ -1, 3), camera_params.camera_int.camera_matrix,
+ camera_params.camera_int.distortion_coeffs)
+
+ ###############
+ ### Compute 3D points on actual training images
+ ### TODO: Add code to do manual point selection
+ ###############
+ AUTO_PROJECTION = True
+
+ if AUTO_PROJECTION:
+ print("\n\nAuto projection of training keypoints to 3D using ideal images")
+ # Match the captured training image against the "ideal" training image
+ # and use those matches to pin down the 3D locations of the keypoints
+
+ for target_ind in range(len(training_target_list)):
+ # Assumes we have 1 ideal view for each training target
+ training_target = training_target_list[target_ind]
+ ideal_target = ideal_target_list[target_ind]
+
+ print("\nPreparing target for image %s" % training_target.image_filename)
+ # Extract keypoints and descriptors for model
+ training_target.extract_features(feature_extractor)
+
+ # Create matcher that we'll use to match with ideal
+ matcher = tam.train_matcher([training_target.descriptor_list])
+
+ matches_list = tam.compute_matches(matcher,
+ [training_target.descriptor_list],
+ [ideal_target.descriptor_list])
+
+ homography_list, matches_mask_list = tam.compute_homographies(
+ [training_target.keypoint_list], [ideal_target.keypoint_list],
+ matches_list)
+
+ for polygon in ideal_target.polygon_list:
+ ideal_pts_2d = np.asarray(np.float32(polygon)).reshape(-1, 1, 2)
+ H_inv = np.linalg.inv(homography_list[0])
+ # We use the ideal target's polygons to define the polygons on
+ # the training target
+ transformed_polygon = cv2.perspectiveTransform(ideal_pts_2d, H_inv)
+ training_target.polygon_list.append(transformed_polygon)
+
+ print("Started with %d keypoints" % len(training_target.keypoint_list))
+
+ training_target.keypoint_list, training_target.descriptor_list, rejected_keypoint_list, rejected_descriptor_list, _ = dtd.filter_keypoints_by_polygons(
+ training_target.keypoint_list, training_target.descriptor_list,
+ training_target.polygon_list)
+ print("After filtering by polygons, had %d keypoints" % len(
+ training_target.keypoint_list))
if VISUALIZE_KEYPOINTS:
- pts = transformed_polygon.astype(int).reshape(-1, 2)
- image = dtd.draw_polygon(training_target.image.copy(), pts,
- (255, 0, 0), True)
- cv2.imshow("image", image)
- cv2.waitKey(0)
+ tam.show_keypoints(training_target.image,
+ training_target.keypoint_list)
- print("Started with %d keypoints" % len(training_target.keypoint_list))
+ # Now comes the fun part
+ # Go through all my training keypoints to define 3D location using ideal
+ training_3d_list = []
+ for kp_ind in range(len(training_target.keypoint_list)):
+ # We're going to look for the first time this keypoint is in a polygon
+ found_3d_loc = False
+ # First, is it in the correct polygon
+ kp_loc = (training_target.keypoint_list[kp_ind].pt[0],
+ training_target.keypoint_list[kp_ind].pt[1])
+ for poly_ind in range(len(training_target.polygon_list)):
+ if dtd.point_in_polygons(
+ kp_loc, [training_target.polygon_list[poly_ind]
+ ]) and not found_3d_loc:
+ found_3d_loc = True
+ # If so, transform keypoint location to ideal using homography, and compute 3D
+ kp_loc_array = np.asarray(np.float32(kp_loc)).reshape(
+ -1, 1, 2)
+ training_2d_in_ideal = cv2.perspectiveTransform(
+ kp_loc_array, homography_list[0])
+ # Get 3D from this 2D point in ideal image
+ training_3d_pt = dtd.compute_3d_points(
+ training_2d_in_ideal,
+ ideal_target.reprojection_map_list[poly_ind])
+ training_3d_list.append(training_3d_pt)
- training_target.keypoint_list, training_target.descriptor_list, rejected_keypoint_list, rejected_descriptor_list, _ = dtd.filter_keypoints_by_polygons(
- training_target.keypoint_list, training_target.descriptor_list,
- training_target.polygon_list)
- print("After filtering by polygons, had %d keypoints" % len(
- training_target.keypoint_list))
- if VISUALIZE_KEYPOINTS:
- tam.show_keypoints(training_target.image,
- training_target.keypoint_list)
+ training_target.keypoint_list_3d = np.asarray(
+ training_3d_list).reshape(-1, 1, 3)
- # Now comes the fun part
- # Go through all my training keypoints to define 3D location using ideal
- training_3d_list = []
- for kp_ind in range(len(training_target.keypoint_list)):
- # We're going to look for the first time this keypoint is in a polygon
- found_3d_loc = False
- # First, is it in the correct polygon
- kp_loc = (training_target.keypoint_list[kp_ind].pt[0],
- training_target.keypoint_list[kp_ind].pt[1])
- for poly_ind in range(len(training_target.polygon_list)):
- if dtd.point_in_polygons(
- kp_loc, [training_target.polygon_list[poly_ind]
- ]) and not found_3d_loc:
- found_3d_loc = True
- # If so, transform keypoint location to ideal using homography, and compute 3D
- kp_loc_array = np.asarray(np.float32(kp_loc)).reshape(
- -1, 1, 2)
- training_2d_in_ideal = cv2.perspectiveTransform(
- kp_loc_array, homography_list[0])
- # Get 3D from this 2D point in ideal image
- training_3d_pt = dtd.compute_3d_points(
- training_2d_in_ideal,
- ideal_target.reprojection_map_list[poly_ind])
- training_3d_list.append(training_3d_pt)
+ if VISUALIZE_KEYPOINTS:
+ # Sanity check these:
+ img_copy = training_target.image.copy()
+ for polygon in training_target.polygon_list:
+ pts = polygon.astype(int).reshape(-1, 2)
+ img_copy = dtd.draw_polygon(img_copy, pts,
+ (255, 0, 0), True)
+ kp_tmp = np.asarray([
+ (kp.pt[0], kp.pt[1]) for kp in training_target.keypoint_list
+ ]).reshape(-1, 2)
+ dtd.visualize_reprojections(
+ img_copy, kp_tmp, training_target.keypoint_list_3d,
+ camera_params.camera_int.camera_matrix,
+ camera_params.camera_int.distortion_coeffs)
- training_target.keypoint_list_3d = np.asarray(
- training_3d_list).reshape(-1, 1, 3)
+ y2020_target_list = training_target_list
+ return y2020_target_list
- if VISUALIZE_KEYPOINTS:
- # Sanity check these:
- img_copy = training_target.image.copy()
- kp_tmp = np.asarray([
- (kp.pt[0], kp.pt[1]) for kp in training_target.keypoint_list
- ]).reshape(-1, 2)
- dtd.visualize_reprojections(
- img_copy, kp_tmp, training_target.keypoint_list_3d,
- camera_params.camera_int.camera_matrix,
- camera_params.camera_int.distortion_coeffs)
+if __name__ == '__main__':
+ ap = argparse.ArgumentParser()
+ ap.add_argument("--visualize", help="Whether to visualize the results", default=False, action='store_true')
+ ap.add_argument("--no_use_bazel", help="Don't run using Bazel", default=True, action='store_false')
+ args = vars(ap.parse_args())
- training_target.save_training_data()
+ VISUALIZE_KEYPOINTS = args["visualize"]
+ if args["visualize"]:
+ print("Visualizing results")
-y2020_target_list = training_target_list
+ USE_BAZEL = args["no_use_bazel"]
+ if args["no_use_bazel"]:
+ print("Running on command line (no Bazel)")
+
+ compute_target_definition()
+ pass
diff --git a/y2020/vision/tools/python_code/test_images/train_power_port_blue.png b/y2020/vision/tools/python_code/test_images/train_power_port_blue.png
index 6ade26f..a3a7597 100644
--- a/y2020/vision/tools/python_code/test_images/train_power_port_blue.png
+++ b/y2020/vision/tools/python_code/test_images/train_power_port_blue.png
Binary files differ
diff --git a/y2020/vision/tools/python_code/train_and_match.py b/y2020/vision/tools/python_code/train_and_match.py
index 276046b..e5a7f5b 100644
--- a/y2020/vision/tools/python_code/train_and_match.py
+++ b/y2020/vision/tools/python_code/train_and_match.py
@@ -3,8 +3,6 @@
import numpy as np
import time
-import field_display
-
### DEFINITIONS
MIN_MATCH_COUNT = 10 # 10 is min; more gives better matches
FEATURE_EXTRACTOR_NAME = 'SIFT'
@@ -281,7 +279,8 @@
# keypoint_list: List of opencv keypoints
def show_keypoints(image, keypoint_list):
ret_img = image.copy()
- ret_img = cv2.drawKeypoints(ret_img, keypoint_list, ret_img)
+ ret_img = cv2.drawKeypoints(ret_img, keypoint_list, ret_img,
+ flags=cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)
cv2.imshow("Keypoints", ret_img)
cv2.waitKey(0)
return ret_img
diff --git a/y2020/vision/tools/python_code/usb_camera_stream.py b/y2020/vision/tools/python_code/usb_camera_stream.py
index ecad9d8..5d3ae91 100644
--- a/y2020/vision/tools/python_code/usb_camera_stream.py
+++ b/y2020/vision/tools/python_code/usb_camera_stream.py
@@ -14,7 +14,7 @@
exp = cap.get(cv2.CAP_PROP_EXPOSURE)
print("Exposure:", exp)
# Display the resulting frame
- cv2.imshow('preview',frame)
+ cv2.imshow('preview', frame)
#Waits for a user input to quit the application
if cv2.waitKey(1) & 0xFF == ord('q'):
